SPAM EMAIL CLASSIFIER

E-mail stands as a highly common communication medium because users can reach anywhere in the world at a reasonable price point and experience speedy message delivery.This is where E-mail spam/ham detection comes into the play,playing a significant role in classifying the emails into spam or ham respectively and thus saving users a lot of time to fetch their E-mails. Spam prevention approaches developed thus far but filtering proves to be the most essential method for stopping spam.technique.So today almost everyone around the globe is using emails with various purposes and hence an efficient growth in the no.of spam emails is witnessed with their genuine/ham emails because of which their precious time is wasted and the system becomes less efficient. The research explores the effectiveness of proposed work through identifying its application methods.This research paper aims to apply the Machine Learning Algorithm i.e. multinomial Naive bayes classifier to classify E-mails into spam or ham.The majority of academic studies focusing on spam filtering deal with advanced classifier-related aspects. In recent days, Machine Spam classification by means of machine learning stands as an essential research topic. The majority of academic studies focusing on spam filtering deal with advanced classifier-related aspects. In recent days, Machine Spam classification by means of machine learning stands as an essential research topic. The research explores the effectiveness of proposed work through identifying its application methods.The research investigates various learning algorithms which detect spam e-mails from the email system. A study comparing different algorithms exists in the document presented.